Useful or Creepy? The Effect of Exchanged Benefits and Information Collection in Social Media Advertisements
Abstract
As digital businesses increasingly use covert methods to collect consumer data for targeted advertisements, many consumers perceive these practices as intrusive. While recent research indicates a willingness to share personal information for rewards, there is limited understanding of the factors motivating such disclosure, particularly in social media advertising. An online survey of 199 U.S. Instagram users revealed that exchanged benefits (e.g., monetary rewards and personalized product recommendations) led to a higher willingness to disclose information through a trade-off between benefit and risk, which in turn led to higher click-through and purchase intentions. However, the proposed moderating role of the information collection method (e.g., overt vs. covert) was not found to be significant. This study offers valuable insights for practitioners, which can be utilized to foster positive consumer attitudes in social media advertising.
Study specs
An online survey was conducted among U.S. Instagram users to assess attitudes toward benefit-risk trade-offs in personal data disclosure for advertising purposes.
- Institution
- University of Minnesota
- Discipline
- Social Science,Marketing
- Sample Size
- N=199
- Study Type
- Survey Research
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Willingness to disclose personal information, click-through intentions, and purchase intentions based on perceived benefits and risks in social media advertisements.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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